Efficient Discovery of Spatial Co-Location Patterns Using the iCPI-tree

With the rapid growth and extensive applications of the spatial dataset, it's getting more important to solve how to find spatial knowledge automatically from spatial datasets. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It's difficult to discovery co-location patterns because of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to identifying the table instances of co-location patterns. The essence of co-location patterns discovery and four co-location patterns mining algorithms proposed in recent years are analyzed, and a new join-less approach for co-location patterns mining, which based on a data structure----iCPI-tree (Improved Co-location Pattern Instance Tree), is proposed. The iCPI-tree is an improved version of the CPI-tree which materializes spatial neighbor relationships in order to accelerate the process of identifying co-location instances. This paper proves the correctness and completeness of the new approach. Finally, an experimental evaluations using synthetic and real world datasets show that the algorithm is computationally more efficient.